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Manufacturing’s End Game in the Artificial Intelligence Journey

The other day someone asked me, “When it comes to Artificial Intelligence and the Industrial Internet of Things (IIoT), when will enough be enough?”

A great deal of hype accompanies emerging technologies, particularly when they hold such promise. That’s why researchers from Gartner created their hype cycle, a representation of the true risks and opportunities during phases of a technology’s journey, a tool that businesses can use to make objective and better decisions.

Yes, there is a lot of grandiose talk around Artificial Intelligence. I tend to understand better through examples. That said, if you were to ask any C-level executive what their facility’s power consumption was during the past 14 days, they wouldn’t know, despite it being one of their larger costs. Understandably, it would take a few emails and days to answer. In the meantime, if there’s an inefficient asset, loss continues to mount.

Getting that insight immediately would lead to better decisions that enhance efficiency and performance. Instant analysis could detect anomalies and trends, even anticipate future issues, leading to preventative measures and perhaps an automated solution.

That’s the end game for Manufacturing in the AI journey.

As widespread as Word

With razor thin profit margins in manufacturing and an increasing need for companies to be agile, decision-makers must be able to perform analytics fast, ultimately in real-time. Artificial Intelligence will fulfill its goal when that day comes. Applying analytics would be as ubiquitous as using Microsoft Word to write documents.

One challenge is for people to “unlearn” some of the hype. That’s the result of Peak of Inflated Expectations that Gartner’s hype cycle warns us about. It requires taking a step back and focusing on fundamentals. We recommend developing a roadmap that identifies the problem and a path to desired results.

You want assets to generate more revenue without further investment or infrastructure upgrades. You don’t want to wait until the end of the month to realize you’ve had issues that drove energy costs sky-high.

You don’t want lagging indicators, you need leading indicators.

Follow the money

It’s all about following how assets impact the bottom line. Artificial Intelligence can map the problem, and with an asset performance management (APM) solution automatically connecting data with financial metrics, you can easily monitor performance, achieve business outcomes, and increase profit margins. Add in Machine Learning and it goes to a whole new level.

With a software intelligently assessing conditions that affect manufacturing processes, it will be able to learn and provide humans with the right information at the right time to make decisions.

A pump gets too hot, sensors detect it, they communicate with the monitoring software, and it predicts what operations need to be shut down before worse damage occurs. The next step would be for the software to dispatch a technician with the details they need to get it up-and-running fast. This will make sure the production down-time is eliminated and operations continue to run efficiently.

Less loss and greater efficiency equals more revenue.

A standard journey

Artificial Intelligence and its industrial application is still relatively young. Exciting things will happen before it reaches its final destination, which for me will be when it becomes standard.

It doesn’t mean removing humans from the process. They’ll be making better decisions based on the best information, from whatever device, no matter where they’re located. Gartner’s hype cycle has its Plateau of Productivity; when a technology becomes widely implemented, its place in the market understood, and its benefits realized.